Overview

Dataset statistics

Number of variables22
Number of observations22229
Missing cells4919
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory176.0 B

Variable types

Numeric10
Text5
DateTime2
Categorical5

Alerts

CITY has constant value ""Constant
STATE has constant value ""Constant
PERMIT NUMBER is highly overall correlated with EXPIRATION DATEHigh correlation
ADDRESS NUMBER START is highly overall correlated with ADDRESS NUMBER and 3 other fieldsHigh correlation
ADDRESS NUMBER is highly overall correlated with ADDRESS NUMBER START and 3 other fieldsHigh correlation
WARD is highly overall correlated with POLICE DISTRICT and 1 other fieldsHigh correlation
POLICE DISTRICT is highly overall correlated with ADDRESS NUMBER START and 3 other fieldsHigh correlation
LATITUDE is highly overall correlated with ADDRESS NUMBER START and 4 other fieldsHigh correlation
LONGITUDE is highly overall correlated with ADDRESS NUMBER START and 2 other fieldsHigh correlation
EXPIRATION DATE is highly overall correlated with PERMIT NUMBERHigh correlation
STREET TYPE is highly imbalanced (51.9%)Imbalance
STREET TYPE has 1360 (6.1%) missing valuesMissing
POLICE DISTRICT has 882 (4.0%) missing valuesMissing
LATITUDE has 882 (4.0%) missing valuesMissing
LONGITUDE has 882 (4.0%) missing valuesMissing
LOCATION has 882 (4.0%) missing valuesMissing
PERMIT NUMBER has unique valuesUnique
ADDRESS NUMBER START has 2120 (9.5%) zerosZeros
ADDRESS NUMBER has 2120 (9.5%) zerosZeros

Reproduction

Analysis started2023-12-06 14:19:49.230622
Analysis finished2023-12-06 14:20:20.579544
Duration31.35 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

PERMIT NUMBER
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22229
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1173805.5
Minimum1000571
Maximum1866738
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:20.727176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1000571
5-th percentile1025232.8
Q11078376
median1111341
Q31133285
95-th percentile1696194
Maximum1866738
Range866167
Interquartile range (IQR)54909

Descriptive statistics

Standard deviation204624.58
Coefficient of variation (CV)0.17432581
Kurtosis2.9827699
Mean1173805.5
Median Absolute Deviation (MAD)25497
Skewness2.1097529
Sum2.6092522 × 1010
Variance4.187122 × 1010
MonotonicityNot monotonic
2023-12-06T14:20:21.088171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1556602 1
 
< 0.1%
1121747 1
 
< 0.1%
1121759 1
 
< 0.1%
1121758 1
 
< 0.1%
1121757 1
 
< 0.1%
1121756 1
 
< 0.1%
1121755 1
 
< 0.1%
1121754 1
 
< 0.1%
1121749 1
 
< 0.1%
1121748 1
 
< 0.1%
Other values (22219) 22219
> 99.9%
ValueCountFrequency (%)
1000571 1
< 0.1%
1001307 1
< 0.1%
1002652 1
< 0.1%
1002993 1
< 0.1%
1003612 1
< 0.1%
1004393 1
< 0.1%
1007248 1
< 0.1%
1007265 1
< 0.1%
1007306 1
< 0.1%
1007406 1
< 0.1%
ValueCountFrequency (%)
1866738 1
< 0.1%
1862206 1
< 0.1%
1860048 1
< 0.1%
1859432 1
< 0.1%
1857612 1
< 0.1%
1855208 1
< 0.1%
1854101 1
< 0.1%
1852797 1
< 0.1%
1852784 1
< 0.1%
1848630 1
< 0.1%

ACCOUNT NUMBER
Real number (ℝ)

Distinct3301
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210983.51
Minimum12
Maximum499543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:21.434767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile5310
Q123392
median261427
Q3352385
95-th percentile421498
Maximum499543
Range499531
Interquartile range (IQR)328993

Descriptive statistics

Standard deviation157608.86
Coefficient of variation (CV)0.7470198
Kurtosis-1.581709
Mean210983.51
Median Absolute Deviation (MAD)137671
Skewness-0.11109042
Sum4.6899524 × 109
Variance2.4840553 × 1010
MonotonicityNot monotonic
2023-12-06T14:20:21.662206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63414 956
 
4.3%
65004 320
 
1.4%
298727 114
 
0.5%
50161 87
 
0.4%
230211 66
 
0.3%
22633 66
 
0.3%
369504 65
 
0.3%
17658 56
 
0.3%
392906 50
 
0.2%
267891 48
 
0.2%
Other values (3291) 20401
91.8%
ValueCountFrequency (%)
12 6
 
< 0.1%
13 23
0.1%
16 4
 
< 0.1%
27 4
 
< 0.1%
28 11
< 0.1%
46 18
0.1%
51 6
 
< 0.1%
66 2
 
< 0.1%
67 20
0.1%
73 17
0.1%
ValueCountFrequency (%)
499543 1
< 0.1%
496618 1
< 0.1%
496437 1
< 0.1%
495513 1
< 0.1%
495456 1
< 0.1%
494737 1
< 0.1%
494369 1
< 0.1%
494267 1
< 0.1%
493688 1
< 0.1%
493639 1
< 0.1%

SITE NUMBER
Real number (ℝ)

Distinct105
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2241666
Minimum1
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:21.935298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile21
Maximum230
Range229
Interquartile range (IQR)1

Descriptive statistics

Standard deviation17.947968
Coefficient of variation (CV)3.4355658
Kurtosis44.381275
Mean5.2241666
Median Absolute Deviation (MAD)0
Skewness6.1902102
Sum116128
Variance322.12957
MonotonicityNot monotonic
2023-12-06T14:20:22.285318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 16347
73.5%
2 2564
 
11.5%
3 661
 
3.0%
4 318
 
1.4%
5 229
 
1.0%
7 124
 
0.6%
6 106
 
0.5%
11 98
 
0.4%
12 96
 
0.4%
19 94
 
0.4%
Other values (95) 1592
 
7.2%
ValueCountFrequency (%)
1 16347
73.5%
2 2564
 
11.5%
3 661
 
3.0%
4 318
 
1.4%
5 229
 
1.0%
6 106
 
0.5%
7 124
 
0.6%
8 65
 
0.3%
9 77
 
0.3%
10 71
 
0.3%
ValueCountFrequency (%)
230 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%
226 1
< 0.1%
225 1
< 0.1%
224 1
< 0.1%
221 1
< 0.1%
220 1
< 0.1%
218 1
< 0.1%
217 1
< 0.1%
Distinct3314
Distinct (%)14.9%
Missing1
Missing (%)< 0.1%
Memory size173.8 KiB
2023-12-06T14:20:22.741847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length67
Median length46
Mean length21.105498
Min length4

Characters and Unicode

Total characters469133
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique726 ?
Unique (%)3.3%

Sample

1st rowTHE LIFEWAY KEFIR SHOP LLC
2nd rowJERRY'S SANDWICHES LS, LLC
3rd rowETTA RIVER NORTH, LLC
4th rowSQUARE KITCHEN, LLC
5th rowROCCO'S, LLC
ValueCountFrequency (%)
inc 9846
 
12.9%
llc 6789
 
8.9%
corporation 1690
 
2.2%
restaurant 1544
 
2.0%
1391
 
1.8%
starbucks 956
 
1.3%
chicago 935
 
1.2%
corp 928
 
1.2%
the 919
 
1.2%
cafe 755
 
1.0%
Other values (3905) 50347
66.2%
2023-12-06T14:20:23.424440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54043
 
11.5%
C 33256
 
7.1%
I 31980
 
6.8%
A 31519
 
6.7%
N 30772
 
6.6%
L 29192
 
6.2%
O 28214
 
6.0%
R 27812
 
5.9%
E 27665
 
5.9%
T 22946
 
4.9%
Other values (68) 151734
32.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 368808
78.6%
Space Separator 54043
 
11.5%
Other Punctuation 29107
 
6.2%
Lowercase Letter 8834
 
1.9%
Decimal Number 7473
 
1.6%
Dash Punctuation 669
 
0.1%
Open Punctuation 92
 
< 0.1%
Close Punctuation 92
 
< 0.1%
Math Symbol 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 33256
 
9.0%
I 31980
 
8.7%
A 31519
 
8.5%
N 30772
 
8.3%
L 29192
 
7.9%
O 28214
 
7.7%
R 27812
 
7.5%
E 27665
 
7.5%
T 22946
 
6.2%
S 22268
 
6.0%
Other values (16) 83184
22.6%
Lowercase Letter
ValueCountFrequency (%)
e 1006
11.4%
a 1004
11.4%
n 882
10.0%
o 736
8.3%
t 661
 
7.5%
r 639
 
7.2%
i 630
 
7.1%
s 563
 
6.4%
c 482
 
5.5%
l 420
 
4.8%
Other values (16) 1811
20.5%
Other Punctuation
ValueCountFrequency (%)
. 12836
44.1%
, 12329
42.4%
' 2488
 
8.5%
& 1222
 
4.2%
# 98
 
0.3%
/ 81
 
0.3%
" 38
 
0.1%
@ 7
 
< 0.1%
! 6
 
< 0.1%
: 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1393
18.6%
2 1052
14.1%
0 942
12.6%
5 890
11.9%
3 848
11.3%
4 793
10.6%
8 522
 
7.0%
6 383
 
5.1%
7 369
 
4.9%
9 281
 
3.8%
Space Separator
ValueCountFrequency (%)
54043
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 669
100.0%
Open Punctuation
ValueCountFrequency (%)
( 92
100.0%
Close Punctuation
ValueCountFrequency (%)
) 92
100.0%
Math Symbol
ValueCountFrequency (%)
+ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 377642
80.5%
Common 91491
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 33256
 
8.8%
I 31980
 
8.5%
A 31519
 
8.3%
N 30772
 
8.1%
L 29192
 
7.7%
O 28214
 
7.5%
R 27812
 
7.4%
E 27665
 
7.3%
T 22946
 
6.1%
S 22268
 
5.9%
Other values (42) 92018
24.4%
Common
ValueCountFrequency (%)
54043
59.1%
. 12836
 
14.0%
, 12329
 
13.5%
' 2488
 
2.7%
1 1393
 
1.5%
& 1222
 
1.3%
2 1052
 
1.1%
0 942
 
1.0%
5 890
 
1.0%
3 848
 
0.9%
Other values (16) 3448
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 469133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54043
 
11.5%
C 33256
 
7.1%
I 31980
 
6.8%
A 31519
 
6.7%
N 30772
 
6.6%
L 29192
 
6.2%
O 28214
 
6.0%
R 27812
 
5.9%
E 27665
 
5.9%
T 22946
 
4.9%
Other values (68) 151734
32.3%
Distinct3384
Distinct (%)15.2%
Missing1
Missing (%)< 0.1%
Memory size173.8 KiB
2023-12-06T14:20:23.789082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length88
Median length48
Mean length16.86454
Min length1

Characters and Unicode

Total characters374865
Distinct characters77
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique714 ?
Unique (%)3.2%

Sample

1st rowLIFEWAY KEFIR SHOP
2nd rowJERRY'S SANDWICHES
3rd rowETTA
4th rowFORK
5th rowRANALLI'S
ValueCountFrequency (%)
2641
 
4.3%
cafe 1547
 
2.5%
the 1500
 
2.4%
coffee 1477
 
2.4%
restaurant 1456
 
2.4%
bar 1287
 
2.1%
grill 1143
 
1.9%
starbucks 973
 
1.6%
inc 829
 
1.3%
and 709
 
1.1%
Other values (3763) 48167
78.0%
2023-12-06T14:20:24.422726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39650
 
10.6%
A 31157
 
8.3%
E 28748
 
7.7%
R 22103
 
5.9%
O 21661
 
5.8%
S 21416
 
5.7%
I 19403
 
5.2%
T 19326
 
5.2%
N 18782
 
5.0%
C 16293
 
4.3%
Other values (67) 136326
36.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 296508
79.1%
Space Separator 39650
 
10.6%
Lowercase Letter 21182
 
5.7%
Other Punctuation 10379
 
2.8%
Decimal Number 6579
 
1.8%
Dash Punctuation 513
 
0.1%
Math Symbol 50
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 31157
 
10.5%
E 28748
 
9.7%
R 22103
 
7.5%
O 21661
 
7.3%
S 21416
 
7.2%
I 19403
 
6.5%
T 19326
 
6.5%
N 18782
 
6.3%
C 16293
 
5.5%
L 15909
 
5.4%
Other values (16) 81710
27.6%
Lowercase Letter
ValueCountFrequency (%)
e 2665
12.6%
a 2577
12.2%
o 1717
 
8.1%
n 1684
 
8.0%
i 1560
 
7.4%
r 1508
 
7.1%
t 1276
 
6.0%
s 1237
 
5.8%
l 1232
 
5.8%
u 792
 
3.7%
Other values (15) 4934
23.3%
Other Punctuation
ValueCountFrequency (%)
' 4575
44.1%
& 2037
19.6%
# 1389
 
13.4%
. 989
 
9.5%
, 669
 
6.4%
/ 620
 
6.0%
" 46
 
0.4%
! 43
 
0.4%
@ 8
 
0.1%
; 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1603
24.4%
1 975
14.8%
4 679
10.3%
3 661
10.0%
5 652
9.9%
0 585
 
8.9%
7 433
 
6.6%
6 380
 
5.8%
8 307
 
4.7%
9 304
 
4.6%
Space Separator
ValueCountFrequency (%)
39650
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 513
100.0%
Math Symbol
ValueCountFrequency (%)
+ 50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 317690
84.7%
Common 57175
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 31157
 
9.8%
E 28748
 
9.0%
R 22103
 
7.0%
O 21661
 
6.8%
S 21416
 
6.7%
I 19403
 
6.1%
T 19326
 
6.1%
N 18782
 
5.9%
C 16293
 
5.1%
L 15909
 
5.0%
Other values (41) 102892
32.4%
Common
ValueCountFrequency (%)
39650
69.3%
' 4575
 
8.0%
& 2037
 
3.6%
2 1603
 
2.8%
# 1389
 
2.4%
. 989
 
1.7%
1 975
 
1.7%
4 679
 
1.2%
, 669
 
1.2%
3 661
 
1.2%
Other values (16) 3948
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 374865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39650
 
10.6%
A 31157
 
8.3%
E 28748
 
7.7%
R 22103
 
5.9%
O 21661
 
5.8%
S 21416
 
5.7%
I 19403
 
5.2%
T 19326
 
5.2%
N 18782
 
5.0%
C 16293
 
4.3%
Other values (67) 136326
36.4%
Distinct3092
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
Minimum2001-03-14 00:00:00
Maximum2023-12-05 00:00:00
2023-12-06T14:20:24.702363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:24.953605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

EXPIRATION DATE
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)0.1%
Missing4
Missing (%)< 0.1%
Memory size173.8 KiB
12/01/2017
 
1227
12/01/2016
 
1221
02/29/2020
 
1198
12/01/2015
 
1196
12/01/2014
 
1193
Other values (24)
16190 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters222250
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row02/28/2022
2nd row02/28/2022
3rd row02/28/2022
4th row02/28/2022
5th row02/28/2022

Common Values

ValueCountFrequency (%)
12/01/2017 1227
 
5.5%
12/01/2016 1221
 
5.5%
02/29/2020 1198
 
5.4%
12/01/2015 1196
 
5.4%
12/01/2014 1193
 
5.4%
12/01/2013 1158
 
5.2%
02/28/2023 1098
 
4.9%
12/01/2012 1098
 
4.9%
05/31/2021 1094
 
4.9%
12/01/2018 1091
 
4.9%
Other values (19) 10651
47.9%

Length

2023-12-06T14:20:25.252436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12/01/2017 1227
 
5.5%
12/01/2016 1221
 
5.5%
02/29/2020 1198
 
5.4%
12/01/2015 1196
 
5.4%
12/01/2014 1193
 
5.4%
12/01/2013 1158
 
5.2%
02/28/2023 1098
 
4.9%
12/01/2012 1098
 
4.9%
05/31/2021 1094
 
4.9%
12/01/2018 1091
 
4.9%
Other values (19) 10651
47.9%

Most occurring characters

ValueCountFrequency (%)
2 53443
24.0%
0 53106
23.9%
1 49799
22.4%
/ 44450
20.0%
8 4466
 
2.0%
3 3989
 
1.8%
5 3015
 
1.4%
9 3002
 
1.4%
4 2950
 
1.3%
7 2051
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177800
80.0%
Other Punctuation 44450
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 53443
30.1%
0 53106
29.9%
1 49799
28.0%
8 4466
 
2.5%
3 3989
 
2.2%
5 3015
 
1.7%
9 3002
 
1.7%
4 2950
 
1.7%
7 2051
 
1.2%
6 1979
 
1.1%
Other Punctuation
ValueCountFrequency (%)
/ 44450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 222250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 53443
24.0%
0 53106
23.9%
1 49799
22.4%
/ 44450
20.0%
8 4466
 
2.0%
3 3989
 
1.8%
5 3015
 
1.4%
9 3002
 
1.4%
4 2950
 
1.3%
7 2051
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 222250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 53443
24.0%
0 53106
23.9%
1 49799
22.4%
/ 44450
20.0%
8 4466
 
2.0%
3 3989
 
1.8%
5 3015
 
1.4%
9 3002
 
1.4%
4 2950
 
1.3%
7 2051
 
0.9%
Distinct3037
Distinct (%)13.7%
Missing22
Missing (%)0.1%
Memory size173.8 KiB
Minimum2001-03-14 00:00:00
Maximum2023-11-16 00:00:00
2023-12-06T14:20:25.905677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:26.366032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2822
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:26.952676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length34
Median length24
Mean length16.555491
Min length10

Characters and Unicode

Total characters368012
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique481 ?
Unique (%)2.2%

Sample

1st row0 W DIVISION ST
2nd row4739 N LINCOLN AVE
3rd row0 N CLARK ST
4th row4600 N LINCOLN AVE
5th row0 N LINCOLN AVE
ValueCountFrequency (%)
n 10709
 
12.1%
st 10586
 
12.0%
ave 8590
 
9.7%
w 7758
 
8.8%
0 2120
 
2.4%
e 1886
 
2.1%
s 1876
 
2.1%
broadway 1201
 
1.4%
clark 1189
 
1.3%
lincoln 1166
 
1.3%
Other values (2005) 41403
46.8%
2023-12-06T14:20:27.777735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66255
18.0%
A 24443
 
6.6%
N 22763
 
6.2%
E 22615
 
6.1%
S 21618
 
5.9%
T 17428
 
4.7%
W 13316
 
3.6%
1 13020
 
3.5%
L 12993
 
3.5%
R 12806
 
3.5%
Other values (26) 140755
38.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 227869
61.9%
Decimal Number 73888
 
20.1%
Space Separator 66255
 
18.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 24443
10.7%
N 22763
10.0%
E 22615
9.9%
S 21618
 
9.5%
T 17428
 
7.6%
W 13316
 
5.8%
L 12993
 
5.7%
R 12806
 
5.6%
O 12376
 
5.4%
I 11990
 
5.3%
Other values (15) 55521
24.4%
Decimal Number
ValueCountFrequency (%)
1 13020
17.6%
0 11569
15.7%
2 9334
12.6%
3 9263
12.5%
5 8189
11.1%
4 7187
9.7%
6 4626
 
6.3%
7 4201
 
5.7%
8 3433
 
4.6%
9 3066
 
4.1%
Space Separator
ValueCountFrequency (%)
66255
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 227869
61.9%
Common 140143
38.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 24443
10.7%
N 22763
10.0%
E 22615
9.9%
S 21618
 
9.5%
T 17428
 
7.6%
W 13316
 
5.8%
L 12993
 
5.7%
R 12806
 
5.6%
O 12376
 
5.4%
I 11990
 
5.3%
Other values (15) 55521
24.4%
Common
ValueCountFrequency (%)
66255
47.3%
1 13020
 
9.3%
0 11569
 
8.3%
2 9334
 
6.7%
3 9263
 
6.6%
5 8189
 
5.8%
4 7187
 
5.1%
6 4626
 
3.3%
7 4201
 
3.0%
8 3433
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66255
18.0%
A 24443
 
6.6%
N 22763
 
6.2%
E 22615
 
6.1%
S 21618
 
5.9%
T 17428
 
4.7%
W 13316
 
3.6%
1 13020
 
3.5%
L 12993
 
3.5%
R 12806
 
3.5%
Other values (26) 140755
38.2%

ADDRESS NUMBER START
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1759
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1701.2908
Minimum0
Maximum11208
Zeros2120
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:28.174811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1217
median1252
Q32732
95-th percentile5024.6
Maximum11208
Range11208
Interquartile range (IQR)2515

Descriptive statistics

Standard deviation1673.0133
Coefficient of variation (CV)0.98337877
Kurtosis0.91675007
Mean1701.2908
Median Absolute Deviation (MAD)1103
Skewness1.1057587
Sum37817994
Variance2798973.5
MonotonicityNot monotonic
2023-12-06T14:20:28.531610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2120
 
9.5%
200 152
 
0.7%
1 106
 
0.5%
111 104
 
0.5%
2100 95
 
0.4%
20 91
 
0.4%
400 90
 
0.4%
175 86
 
0.4%
731 81
 
0.4%
100 75
 
0.3%
Other values (1749) 19229
86.5%
ValueCountFrequency (%)
0 2120
9.5%
1 106
 
0.5%
2 21
 
0.1%
5 17
 
0.1%
6 63
 
0.3%
7 7
 
< 0.1%
8 23
 
0.1%
9 17
 
0.1%
10 44
 
0.2%
11 9
 
< 0.1%
ValueCountFrequency (%)
11208 1
 
< 0.1%
11057 1
 
< 0.1%
10701 10
< 0.1%
10533 2
 
< 0.1%
10448 1
 
< 0.1%
9710 1
 
< 0.1%
8753 1
 
< 0.1%
8548 9
< 0.1%
8301 1
 
< 0.1%
8300 1
 
< 0.1%

ADDRESS NUMBER
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1759
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1701.2908
Minimum0
Maximum11208
Zeros2120
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:28.968449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1217
median1252
Q32732
95-th percentile5024.6
Maximum11208
Range11208
Interquartile range (IQR)2515

Descriptive statistics

Standard deviation1673.0133
Coefficient of variation (CV)0.98337877
Kurtosis0.91675007
Mean1701.2908
Median Absolute Deviation (MAD)1103
Skewness1.1057587
Sum37817994
Variance2798973.5
MonotonicityNot monotonic
2023-12-06T14:20:29.274815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2120
 
9.5%
200 152
 
0.7%
1 106
 
0.5%
111 104
 
0.5%
2100 95
 
0.4%
20 91
 
0.4%
400 90
 
0.4%
175 86
 
0.4%
731 81
 
0.4%
100 75
 
0.3%
Other values (1749) 19229
86.5%
ValueCountFrequency (%)
0 2120
9.5%
1 106
 
0.5%
2 21
 
0.1%
5 17
 
0.1%
6 63
 
0.3%
7 7
 
< 0.1%
8 23
 
0.1%
9 17
 
0.1%
10 44
 
0.2%
11 9
 
< 0.1%
ValueCountFrequency (%)
11208 1
 
< 0.1%
11057 1
 
< 0.1%
10701 10
< 0.1%
10533 2
 
< 0.1%
10448 1
 
< 0.1%
9710 1
 
< 0.1%
8753 1
 
< 0.1%
8548 9
< 0.1%
8301 1
 
< 0.1%
8300 1
 
< 0.1%

STREET DIRECTION
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
N
10709 
W
7758 
E
1886 
S
1876 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22229
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 10709
48.2%
W 7758
34.9%
E 1886
 
8.5%
S 1876
 
8.4%

Length

2023-12-06T14:20:29.502359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T14:20:29.694669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
n 10709
48.2%
w 7758
34.9%
e 1886
 
8.5%
s 1876
 
8.4%

Most occurring characters

ValueCountFrequency (%)
N 10709
48.2%
W 7758
34.9%
E 1886
 
8.5%
S 1876
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 22229
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 10709
48.2%
W 7758
34.9%
E 1886
 
8.5%
S 1876
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 22229
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 10709
48.2%
W 7758
34.9%
E 1886
 
8.5%
S 1876
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 10709
48.2%
W 7758
34.9%
E 1886
 
8.5%
S 1876
 
8.4%

STREET
Text

Distinct233
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:30.079772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length17
Mean length7.0250124
Min length3

Characters and Unicode

Total characters156159
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.1%

Sample

1st rowDIVISION
2nd rowLINCOLN
3rd rowCLARK
4th rowLINCOLN
5th rowLINCOLN
ValueCountFrequency (%)
broadway 1201
 
5.2%
clark 1189
 
5.1%
lincoln 1166
 
5.0%
wells 1140
 
4.9%
division 941
 
4.1%
michigan 729
 
3.1%
milwaukee 693
 
3.0%
southport 667
 
2.9%
randolph 658
 
2.8%
state 538
 
2.3%
Other values (235) 14235
61.5%
2023-12-06T14:20:30.811192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 15853
 
10.2%
L 12452
 
8.0%
O 12376
 
7.9%
E 12139
 
7.8%
N 12054
 
7.7%
I 11990
 
7.7%
R 11948
 
7.7%
S 9196
 
5.9%
D 7357
 
4.7%
T 6801
 
4.4%
Other values (25) 43993
28.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 154290
98.8%
Decimal Number 941
 
0.6%
Space Separator 928
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15853
 
10.3%
L 12452
 
8.1%
O 12376
 
8.0%
E 12139
 
7.9%
N 12054
 
7.8%
I 11990
 
7.8%
R 11948
 
7.7%
S 9196
 
6.0%
D 7357
 
4.8%
T 6801
 
4.4%
Other values (15) 42124
27.3%
Decimal Number
ValueCountFrequency (%)
3 314
33.4%
5 228
24.2%
1 147
15.6%
8 60
 
6.4%
6 56
 
6.0%
7 49
 
5.2%
2 48
 
5.1%
9 26
 
2.8%
4 13
 
1.4%
Space Separator
ValueCountFrequency (%)
928
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 154290
98.8%
Common 1869
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15853
 
10.3%
L 12452
 
8.1%
O 12376
 
8.0%
E 12139
 
7.9%
N 12054
 
7.8%
I 11990
 
7.8%
R 11948
 
7.7%
S 9196
 
6.0%
D 7357
 
4.8%
T 6801
 
4.4%
Other values (15) 42124
27.3%
Common
ValueCountFrequency (%)
928
49.7%
3 314
 
16.8%
5 228
 
12.2%
1 147
 
7.9%
8 60
 
3.2%
6 56
 
3.0%
7 49
 
2.6%
2 48
 
2.6%
9 26
 
1.4%
4 13
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15853
 
10.2%
L 12452
 
8.0%
O 12376
 
7.9%
E 12139
 
7.8%
N 12054
 
7.7%
I 11990
 
7.7%
R 11948
 
7.7%
S 9196
 
5.9%
D 7357
 
4.7%
T 6801
 
4.4%
Other values (25) 43993
28.2%

STREET TYPE
Categorical

IMBALANCE  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing1360
Missing (%)6.1%
Memory size173.8 KiB
ST
10546 
AVE
8590 
RD
 
658
BLVD
 
280
PL
 
261
Other values (4)
 
534

Length

Max length4
Median length2
Mean length2.4605875
Min length2

Characters and Unicode

Total characters51350
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowST
2nd rowAVE
3rd rowST
4th rowAVE
5th rowAVE

Common Values

ValueCountFrequency (%)
ST 10546
47.4%
AVE 8590
38.6%
RD 658
 
3.0%
BLVD 280
 
1.3%
PL 261
 
1.2%
PKWY 209
 
0.9%
DR 200
 
0.9%
CT 81
 
0.4%
HWY 44
 
0.2%
(Missing) 1360
 
6.1%

Length

2023-12-06T14:20:31.184311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T14:20:31.465364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
st 10546
50.5%
ave 8590
41.2%
rd 658
 
3.2%
blvd 280
 
1.3%
pl 261
 
1.3%
pkwy 209
 
1.0%
dr 200
 
1.0%
ct 81
 
0.4%
hwy 44
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 10627
20.7%
S 10546
20.5%
V 8870
17.3%
A 8590
16.7%
E 8590
16.7%
D 1138
 
2.2%
R 858
 
1.7%
L 541
 
1.1%
P 470
 
0.9%
B 280
 
0.5%
Other values (5) 840
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 51350
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 10627
20.7%
S 10546
20.5%
V 8870
17.3%
A 8590
16.7%
E 8590
16.7%
D 1138
 
2.2%
R 858
 
1.7%
L 541
 
1.1%
P 470
 
0.9%
B 280
 
0.5%
Other values (5) 840
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 51350
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 10627
20.7%
S 10546
20.5%
V 8870
17.3%
A 8590
16.7%
E 8590
16.7%
D 1138
 
2.2%
R 858
 
1.7%
L 541
 
1.1%
P 470
 
0.9%
B 280
 
0.5%
Other values (5) 840
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 10627
20.7%
S 10546
20.5%
V 8870
17.3%
A 8590
16.7%
E 8590
16.7%
D 1138
 
2.2%
R 858
 
1.7%
L 541
 
1.1%
P 470
 
0.9%
B 280
 
0.5%
Other values (5) 840
 
1.6%

CITY
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
CHICAGO
22229 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters155603
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHICAGO
2nd rowCHICAGO
3rd rowCHICAGO
4th rowCHICAGO
5th rowCHICAGO

Common Values

ValueCountFrequency (%)
CHICAGO 22229
100.0%

Length

2023-12-06T14:20:31.722504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T14:20:31.931040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
chicago 22229
100.0%

Most occurring characters

ValueCountFrequency (%)
C 44458
28.6%
H 22229
14.3%
I 22229
14.3%
A 22229
14.3%
G 22229
14.3%
O 22229
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 155603
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 44458
28.6%
H 22229
14.3%
I 22229
14.3%
A 22229
14.3%
G 22229
14.3%
O 22229
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 155603
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 44458
28.6%
H 22229
14.3%
I 22229
14.3%
A 22229
14.3%
G 22229
14.3%
O 22229
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 44458
28.6%
H 22229
14.3%
I 22229
14.3%
A 22229
14.3%
G 22229
14.3%
O 22229
14.3%

STATE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
IL
22229 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters44458
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIL
2nd rowIL
3rd rowIL
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
IL 22229
100.0%

Length

2023-12-06T14:20:32.142391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T14:20:32.340746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
il 22229
100.0%

Most occurring characters

ValueCountFrequency (%)
I 22229
50.0%
L 22229
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 44458
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 22229
50.0%
L 22229
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44458
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 22229
50.0%
L 22229
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 22229
50.0%
L 22229
50.0%

ZIP CODE
Real number (ℝ)

Distinct53
Distinct (%)0.2%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean60626.852
Minimum60601
Maximum60707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:32.600132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum60601
5-th percentile60603
Q160611
median60618
Q360647
95-th percentile60657
Maximum60707
Range106
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.303309
Coefficient of variation (CV)0.00033488971
Kurtosis-0.95237657
Mean60626.852
Median Absolute Deviation (MAD)11
Skewness0.57212698
Sum1.3474924 × 109
Variance412.22434
MonotonicityNot monotonic
2023-12-06T14:20:32.885340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60657 2235
 
10.1%
60611 1869
 
8.4%
60654 1774
 
8.0%
60622 1759
 
7.9%
60614 1664
 
7.5%
60607 1328
 
6.0%
60613 1170
 
5.3%
60647 967
 
4.4%
60610 952
 
4.3%
60618 908
 
4.1%
Other values (43) 7600
34.2%
ValueCountFrequency (%)
60601 656
3.0%
60602 410
 
1.8%
60603 331
 
1.5%
60604 234
 
1.1%
60605 729
3.3%
60606 385
 
1.7%
60607 1328
6.0%
60608 213
 
1.0%
60609 32
 
0.1%
60610 952
4.3%
ValueCountFrequency (%)
60707 47
 
0.2%
60661 506
 
2.3%
60660 350
 
1.6%
60659 203
 
0.9%
60657 2235
10.1%
60656 17
 
0.1%
60655 3
 
< 0.1%
60654 1774
8.0%
60653 23
 
0.1%
60651 3
 
< 0.1%

WARD
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.353862
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:33.219132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q127
median42
Q344
95-th percentile47
Maximum50
Range49
Interquartile range (IQR)17

Descriptive statistics

Standard deviation16.140922
Coefficient of variation (CV)0.49888702
Kurtosis-0.46189472
Mean32.353862
Median Absolute Deviation (MAD)5
Skewness-1.0438656
Sum719194
Variance260.52936
MonotonicityNot monotonic
2023-12-06T14:20:33.557384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
42 5595
25.2%
44 2204
 
9.9%
27 1699
 
7.6%
1 1632
 
7.3%
47 1601
 
7.2%
2 1392
 
6.3%
43 1278
 
5.7%
32 1193
 
5.4%
4 672
 
3.0%
46 624
 
2.8%
Other values (34) 4339
19.5%
ValueCountFrequency (%)
1 1632
7.3%
2 1392
6.3%
3 293
 
1.3%
4 672
3.0%
5 104
 
0.5%
6 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 13
 
0.1%
11 339
 
1.5%
ValueCountFrequency (%)
50 109
 
0.5%
49 164
 
0.7%
48 606
 
2.7%
47 1601
 
7.2%
46 624
 
2.8%
45 270
 
1.2%
44 2204
 
9.9%
43 1278
 
5.7%
42 5595
25.2%
41 91
 
0.4%

POLICE DISTRICT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)0.1%
Missing882
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean14.11566
Minimum0
Maximum25
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:33.815777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median18
Q319
95-th percentile20
Maximum25
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.7903564
Coefficient of variation (CV)0.48105128
Kurtosis-0.23988417
Mean14.11566
Median Absolute Deviation (MAD)1
Skewness-1.0661001
Sum301327
Variance46.10894
MonotonicityNot monotonic
2023-12-06T14:20:34.136458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
18 5345
24.0%
19 5064
22.8%
1 3661
16.5%
12 2508
11.3%
14 1789
 
8.0%
20 988
 
4.4%
16 419
 
1.9%
17 374
 
1.7%
24 364
 
1.6%
2 239
 
1.1%
Other values (13) 596
 
2.7%
(Missing) 882
 
4.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3661
16.5%
2 239
 
1.1%
3 10
 
< 0.1%
4 15
 
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 44
 
0.2%
9 224
 
1.0%
ValueCountFrequency (%)
25 149
 
0.7%
24 364
 
1.6%
22 27
 
0.1%
20 988
 
4.4%
19 5064
22.8%
18 5345
24.0%
17 374
 
1.7%
16 419
 
1.9%
15 7
 
< 0.1%
14 1789
 
8.0%

LATITUDE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2728
Distinct (%)12.8%
Missing882
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean41.912355
Minimum41.69067
Maximum42.019421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-12-06T14:20:34.463867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum41.69067
5-th percentile41.86652
Q141.885856
median41.903246
Q341.942224
95-th percentile41.978607
Maximum42.019421
Range0.32875146
Interquartile range (IQR)0.056368381

Descriptive statistics

Standard deviation0.038256308
Coefficient of variation (CV)0.00091276923
Kurtosis1.5806341
Mean41.912355
Median Absolute Deviation (MAD)0.021535653
Skewness-0.14538575
Sum894703.04
Variance0.0014635451
MonotonicityNot monotonic
2023-12-06T14:20:34.802466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.88200199 98
 
0.4%
41.88197573 96
 
0.4%
41.90405052 86
 
0.4%
41.87801449 81
 
0.4%
41.88460018 69
 
0.3%
41.8825402 53
 
0.2%
41.90186736 40
 
0.2%
41.89678605 40
 
0.2%
41.87949547 37
 
0.2%
41.88216417 37
 
0.2%
Other values (2718) 20710
93.2%
(Missing) 882
 
4.0%
ValueCountFrequency (%)
41.69066951 1
 
< 0.1%
41.69139989 2
 
< 0.1%
41.69245222 1
 
< 0.1%
41.69920305 10
< 0.1%
41.70289718 1
 
< 0.1%
41.70356373 2
 
< 0.1%
41.71874411 1
 
< 0.1%
41.72107515 10
< 0.1%
41.72112515 1
 
< 0.1%
41.72177014 1
 
< 0.1%
ValueCountFrequency (%)
42.01942097 12
0.1%
42.0193885 5
 
< 0.1%
42.01934594 4
 
< 0.1%
42.01933013 3
 
< 0.1%
42.01932963 2
 
< 0.1%
42.0193098 4
 
< 0.1%
42.01927235 1
 
< 0.1%
42.0174068 8
< 0.1%
42.01615704 15
0.1%
42.01615267 15
0.1%

LONGITUDE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2728
Distinct (%)12.8%
Missing882
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean-87.654977
Minimum-87.834308
Maximum-87.535139
Zeros0
Zeros (%)0.0%
Negative21347
Negative (%)96.0%
Memory size173.8 KiB
2023-12-06T14:20:35.069032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-87.834308
5-th percentile-87.708499
Q1-87.670876
median-87.648725
Q3-87.630567
95-th percentile-87.624293
Maximum-87.535139
Range0.29916895
Interquartile range (IQR)0.040309124

Descriptive statistics

Standard deviation0.032511036
Coefficient of variation (CV)-0.00037089777
Kurtosis5.6208684
Mean-87.654977
Median Absolute Deviation (MAD)0.0196747
Skewness-1.8308208
Sum-1871170.8
Variance0.0010569674
MonotonicityNot monotonic
2023-12-06T14:20:35.434224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.63103164 98
 
0.4%
-87.63397185 96
 
0.4%
-87.62874675 86
 
0.4%
-87.63318903 81
 
0.4%
-87.62798897 69
 
0.3%
-87.62453095 53
 
0.2%
-87.62849214 40
 
0.2%
-87.62828088 40
 
0.2%
-87.63382966 37
 
0.2%
-87.62451427 37
 
0.2%
Other values (2718) 20710
93.2%
(Missing) 882
 
4.0%
ValueCountFrequency (%)
-87.8343079 1
 
< 0.1%
-87.82625503 9
< 0.1%
-87.82618448 1
 
< 0.1%
-87.82167425 5
 
< 0.1%
-87.82042719 4
 
< 0.1%
-87.81865423 16
0.1%
-87.81795264 4
 
< 0.1%
-87.81783297 3
 
< 0.1%
-87.81729036 11
< 0.1%
-87.8172596 2
 
< 0.1%
ValueCountFrequency (%)
-87.53513895 2
 
< 0.1%
-87.55117213 1
 
< 0.1%
-87.55124869 1
 
< 0.1%
-87.55161886 9
< 0.1%
-87.56729719 2
 
< 0.1%
-87.58184369 4
< 0.1%
-87.58390766 1
 
< 0.1%
-87.58502961 2
 
< 0.1%
-87.58781452 8
< 0.1%
-87.58797399 7
< 0.1%

LOCATION
Text

MISSING 

Distinct2728
Distinct (%)12.8%
Missing882
Missing (%)4.0%
Memory size173.8 KiB
2023-12-06T14:20:35.788157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length40
Median length39
Mean length39.102216
Min length35

Characters and Unicode

Total characters834715
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique466 ?
Unique (%)2.2%

Sample

1st row(41.90405051948726, -87.62874675447662)
2nd row(41.96769881732379, -87.68780818484225)
3rd row(41.88200198545344, -87.6310316367502)
4th row(41.964902360748326, -87.68627917084095)
5th row(41.94330292584782, -87.67135515305324)
ValueCountFrequency (%)
41.88200198545344 98
 
0.2%
87.6310316367502 98
 
0.2%
41.881975727713886 96
 
0.2%
87.63397184627037 96
 
0.2%
41.90405051948726 86
 
0.2%
87.62874675447662 86
 
0.2%
41.878014487249544 81
 
0.2%
87.63318903001444 81
 
0.2%
87.62798896732363 69
 
0.2%
41.884600177780484 69
 
0.2%
Other values (5446) 41834
98.0%
2023-12-06T14:20:36.388308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 89124
10.7%
4 80725
9.7%
7 78215
9.4%
6 76523
9.2%
1 74206
8.9%
9 66936
8.0%
2 56948
 
6.8%
3 56186
 
6.7%
5 55910
 
6.7%
0 50513
 
6.1%
Other values (6) 149429
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 685286
82.1%
Other Punctuation 64041
 
7.7%
Open Punctuation 21347
 
2.6%
Space Separator 21347
 
2.6%
Dash Punctuation 21347
 
2.6%
Close Punctuation 21347
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 89124
13.0%
4 80725
11.8%
7 78215
11.4%
6 76523
11.2%
1 74206
10.8%
9 66936
9.8%
2 56948
8.3%
3 56186
8.2%
5 55910
8.2%
0 50513
7.4%
Other Punctuation
ValueCountFrequency (%)
. 42694
66.7%
, 21347
33.3%
Open Punctuation
ValueCountFrequency (%)
( 21347
100.0%
Space Separator
ValueCountFrequency (%)
21347
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21347
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21347
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 834715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 89124
10.7%
4 80725
9.7%
7 78215
9.4%
6 76523
9.2%
1 74206
8.9%
9 66936
8.0%
2 56948
 
6.8%
3 56186
 
6.7%
5 55910
 
6.7%
0 50513
 
6.1%
Other values (6) 149429
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 834715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 89124
10.7%
4 80725
9.7%
7 78215
9.4%
6 76523
9.2%
1 74206
8.9%
9 66936
8.0%
2 56948
 
6.8%
3 56186
 
6.7%
5 55910
 
6.7%
0 50513
 
6.1%
Other values (6) 149429
17.9%

Interactions

2023-12-06T14:20:16.499873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:54.578885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:57.029934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:59.264915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:01.897558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:05.937937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:08.682290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:10.485398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:12.444465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:14.396587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:16.695200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:54.888140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:57.337009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:59.543332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:02.177267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:06.276822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:08.938868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:10.652639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:12.666308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:14.679253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:16.980310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:55.051801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:57.566408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:59.763980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:02.829402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:06.576028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:09.086058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:10.847202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:12.873374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:14.908923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:17.172995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:55.242174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:57.750474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:59.909055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:03.154544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:06.863570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:09.282276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:11.030884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:13.075046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:15.106078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:17.682938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:55.430840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:57.918689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:00.098039image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:03.637862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:07.215378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:09.482125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:11.200908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:13.244247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:15.325650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:17.917642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:55.655978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:58.121483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:00.432223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:04.108172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:07.446479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:09.629289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:11.398171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:13.419374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:15.499115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:18.095784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:55.849865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:58.299916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:00.743167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:04.485538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:07.703490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:09.817593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:11.611783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:13.612209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:15.719444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:18.294760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:56.142159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:58.573340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:01.023434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:05.039342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:07.932250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:10.008715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:11.861130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:13.775500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:15.928157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:18.455078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:56.476310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:58.759713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:01.389461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:05.278053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:08.159759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:10.171181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:12.050725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:13.942861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:16.129722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:18.672819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:56.721592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:19:59.002945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:01.654160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:05.642552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:08.422200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:10.333955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:12.237088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:14.195037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:20:16.333649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-06T14:20:36.618125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
PERMIT NUMBERACCOUNT NUMBERSITE NUMBERADDRESS NUMBER STARTADDRESS NUMBERZIP CODEWARDPOLICE DISTRICTLATITUDELONGITUDEEXPIRATION DATESTREET DIRECTIONSTREET TYPE
PERMIT NUMBER1.0000.4730.011-0.255-0.255-0.003-0.052-0.149-0.1360.0370.8710.0290.001
ACCOUNT NUMBER0.4731.000-0.185-0.141-0.141-0.035-0.087-0.143-0.1240.0170.2700.0700.068
SITE NUMBER0.011-0.1851.000-0.107-0.107-0.0890.060-0.053-0.0720.1430.0320.0690.056
ADDRESS NUMBER START-0.255-0.141-0.1071.0001.0000.3610.2270.5610.734-0.7200.1010.2980.197
ADDRESS NUMBER-0.255-0.141-0.1071.0001.0000.3610.2270.5610.734-0.7200.1010.2980.197
ZIP CODE-0.003-0.035-0.0890.3610.3611.0000.2470.4880.475-0.4440.0120.3140.225
WARD-0.052-0.0870.0600.2270.2270.2471.0000.5630.518-0.0700.0190.2960.167
POLICE DISTRICT-0.149-0.143-0.0530.5610.5610.4880.5631.0000.829-0.3420.0800.3920.186
LATITUDE-0.136-0.124-0.0720.7340.7340.4750.5180.8291.000-0.6210.0890.3920.212
LONGITUDE0.0370.0170.143-0.720-0.720-0.444-0.070-0.342-0.6211.0000.0410.3170.268
EXPIRATION DATE0.8710.2700.0320.1010.1010.0120.0190.0800.0890.0411.0000.0170.000
STREET DIRECTION0.0290.0700.0690.2980.2980.3140.2960.3920.3920.3170.0171.0000.242
STREET TYPE0.0010.0680.0560.1970.1970.2250.1670.1860.2120.2680.0000.2421.000

Missing values

2023-12-06T14:20:19.022283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-06T14:20:19.794935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-06T14:20:20.265332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PERMIT NUMBERACCOUNT NUMBERSITE NUMBERLEGAL NAMEDOING BUSINESS AS NAMEISSUED DATEEXPIRATION DATEPAYMENT DATEADDRESSADDRESS NUMBER STARTADDRESS NUMBERSTREET DIRECTIONSTREETSTREET TYPECITYSTATEZIP CODEWARDPOLICE DISTRICTLATITUDELONGITUDELOCATION
015566023289921THE LIFEWAY KEFIR SHOP LLCLIFEWAY KEFIR SHOP07/16/202102/28/202207/16/20210 W DIVISION ST00WDIVISIONSTCHICAGOIL60622.0118.041.904051-87.628747(41.90405051948726, -87.62874675447662)
115313033994981JERRY'S SANDWICHES LS, LLCJERRY'S SANDWICHES07/16/202102/28/202207/16/20214739 N LINCOLN AVE47394739NLINCOLNAVECHICAGOIL60625.04719.041.967699-87.687808(41.96769881732379, -87.68780818484225)
215530784631881ETTA RIVER NORTH, LLCETTA07/16/202102/28/202207/16/20210 N CLARK ST00NCLARKSTCHICAGOIL60654.021.041.882002-87.631032(41.88200198545344, -87.6310316367502)
315345562527421SQUARE KITCHEN, LLCFORK07/16/202102/28/202207/16/20214600 N LINCOLN AVE46004600NLINCOLNAVECHICAGOIL60625.04719.041.964902-87.686279(41.964902360748326, -87.68627917084095)
415560063371781ROCCO'S, LLCRANALLI'S07/16/202102/28/202207/16/20210 N LINCOLN AVE00NLINCOLNAVECHICAGOIL60614.043NaNNaNNaNNone
515366214144141BBSC #4 LLCBROWN BAG SEAFOOD CO.07/16/202102/28/202207/16/20213400 N LINCOLN AVE34003400NLINCOLNAVECHICAGOIL60657.04719.041.943303-87.671355(41.94330292584782, -87.67135515305324)
61559950340631GASTHAUS ZUM LOEWEN, INC.THE REVELER07/16/202102/28/202207/16/20210 W ROSCOE ST00WROSCOESTCHICAGOIL60657.032NaNNaNNaNNone
71540360239571TEMPO CAFE LIMITEDTEMPO CAFE07/16/202102/28/202207/15/20216 E CHESTNUT ST66ECHESTNUTSTCHICAGOIL60611.0218.041.898431-87.628009(41.89843137207629, -87.6280091630558)
815433494255401MI FOGATA INC.MI FOGATA INC.07/16/202102/28/202207/16/20214322 N WESTERN AVE43224322NWESTERNAVECHICAGOIL60618.04719.041.960229-87.688800(41.96022917610446, -87.68880023680377)
915551873401261SHINE RESTAURANT CORP.SHINE RESTAURANT, RISE SUSHI RESTAURANT07/17/202102/28/202207/14/20210 W WEBSTER AVE00WWEBSTERAVECHICAGOIL60614.043NaNNaNNaNNone
PERMIT NUMBERACCOUNT NUMBERSITE NUMBERLEGAL NAMEDOING BUSINESS AS NAMEISSUED DATEEXPIRATION DATEPAYMENT DATEADDRESSADDRESS NUMBER STARTADDRESS NUMBERSTREET DIRECTIONSTREETSTREET TYPECITYSTATEZIP CODEWARDPOLICE DISTRICTLATITUDELONGITUDELOCATION
2221918380274152101AREPA GEORGE LPAREPA GEORGE07/24/202302/29/202407/24/20230 N KEDZIE AVE00NKEDZIEAVECHICAGOIL60651.02611.041.881016-87.706271(41.881015948678254, -87.7062708302215)
2222018070404773771SILA'S MEDITERRANEAN, INC.SILA'S MEDITERRANEAN07/24/202302/29/202407/24/20230 N BROADWAY00NBROADWAYNoneCHICAGOIL60657.044NaNNaNNaNNone
2222118454003701965CHICAGO LOCAL MARKETS LLCROOST CHICKEN & BISCUITS07/24/202302/29/202407/24/2023455 N MILWAUKEE AVE455455NMILWAUKEEAVECHICAGOIL60654.02712.041.890246-87.645909(41.890245551519065, -87.64590936487818)
22222178003959489FRONTERA GRILL, INC.FRONTERA GRILL/TOPOLOBAMPO/BAR SOTANO07/24/202302/29/202407/24/20230 N CLARK ST00NCLARKSTCHICAGOIL60654.0421.041.882002-87.631032(41.88200198545344, -87.6310316367502)
2222317796093933071ALULU LLCALULU07/25/202302/29/202407/25/20230 S LAFLIN ST00SLAFLINSTCHICAGOIL60608.02512.041.881448-87.664564(41.8814479951026, -87.6645642415278)
2222418248484919751DIVISION BAR AND RESTAURANT LLCDesert Hawk07/26/202302/29/202407/26/20232049 W DIVISION ST20492049WDIVISIONSTCHICAGOIL60622.0112.041.903046-87.679195(41.903045577773014, -87.67919511353082)
2222518075684942671NESH MEDITERRANEAN GRILL LLCNESH MEDITERRANEAN GRILL07/26/202302/29/202407/26/2023205 W MONROE ST205205WMONROESTCHICAGOIL60606.0421.041.880548-87.633981(41.880547897806615, -87.63398066695044)
2222617996062828361VOLO RESTAURANT LLCVOLO RESTAURANT07/26/202302/29/202407/26/20230 W ROSCOE ST00WROSCOESTCHICAGOIL60618.032NaNNaNNaNNone
222271805009210101KITSCH'N ON ROSCOE, INC.KITSCH'N ON ROSCOE07/28/202302/29/202407/28/20232005 W ROSCOE ST20052005WROSCOESTCHICAGOIL60618.03219.041.943105-87.678679(41.94310456354472, -87.67867885196078)
22228178309442454321253 W 18TH STREET, LLCSushi Hoshi07/28/202302/29/202407/28/20230 S LAFLIN ST00SLAFLINSTCHICAGOIL60608.02512.041.881448-87.664564(41.8814479951026, -87.6645642415278)